An intelligent goal-seeking agent situated in the real world should make decisions along various timescales to act and plan efficiently.
A natural approach that facilitates this behavior is via a divide-and-conquer strategy, where goals are decomposed along sub-goals, and solutions to such sub-goals (i.e. `correct' sequences of actions) are stored as reusable `primitive' sub-routines [1]}, [2]}, [3]}, [4]}.
Viewing complex goal-directed behavior as (novel) compositions of known sub-routines simplifies both decision-making and planning [5]}, [6]}, while also benefiting out-of-distribution generalization [7]}.
